This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Standard image reconstruction algorithms for transmission tomography are based upon linear models that omit noise and make overly simplistic linear perturbation assumptions. These algorithms have the benefit of being noniterative. In contrast, algorithms based on maximizing likelihood take into account the underlying physics and noise models. These algorithms have the cost of being iterative. The additional benefits of these algorithms in terms of image quality and quantitative accuracy motivate seeking efficient implementations and quantitative performance prediction. These are the goals of this project. We are actively developing parallel implementations to achieve sufficiently high efficiency that: (1) performance can be evaluated through simulations and compared to analytical predictions;(2) image quality can be evaluated both quantitatively and qualitatively;and (3) implementations in clinically relevant times are possible.